RankASOEscape, in public: one indie app's ASO, month by month
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This is the running record of how our own app Escape, an adult content blocker for iPhone, is optimised for App Store search, with the actual ranks, the actual keyword fields and the decisions behind each change. It is the app people kept asking about, and the reason RankASO exists. Every number is from RankASO's daily checks, with the date. The posts linked throughout explain the methods; this page is what they produced. It grows with every version.
The starting position, October 31, 2026
Forty-nine tracked terms in the US store, with Canada and the UK alongside. The headline: six number ones, all carried by the name and subtitle alone. "adult content blocker", "adult site blocker", "content blocker", "blocker", "corn blocker" and "block corn", at the top of the US result with no keyword field characters spent. Why the name does that work.
Below the number ones, the shape was a mid-tail gap: "block adult content" at 3 behind a rival at number one, "temptation blocker" at 2, "quit corn" at 11, "site blocker" at 17, "chrome blocker" at 19, and a cluster of terms between 60 and past 200 where field words were earning little. The field report said that of the fifteen field words, three earned nothing worth keeping: "safari", "lust" and "reboot". How the report reads.
The wide check across storefronts turned up the surprise: Canada held eighteen of twenty top-10 terms with five number ones, on a small fraction of the downloads. The UK and India, which read the same English (UK) field, sat at thirteen and fourteen of twenty. Picking storefronts.
The rival that left, late October
One of Escape's tracked rivals disappeared from the store. The removal watch listed the terms it had held; its brand stayed out of our listing, and the intent phrases among its terms went onto the list for the next field. What that week looks like.
The field change, November 3, 2026
Version 5.12's en-US field, as entered: filter,addiction,chrome,recovery,urge,no nut,overcome,temptation,website,web,nnn,gooning,relapse,pmo, 100 characters. Out: "safari", "lust", "reboot". In: "gooning", "relapse", "pmo". The first draft ended in "app"; the rule checker flagged it as a word the store ignores, and the four characters went to "pmo", a term the app already ranked 92 for in the US and 64 in the UK. The French Canadian field took "sobriety" and "screen time", English words Canada reads through it. The field, line by line. What each storefront reads.
Baseline recorded the same day, before the version shipped: "abstinence" 62 US, 46 CA, 32 UK; "nnn" 31 US; "pmo" 92 US, 66 CA, 64 UK; "reboot" 60 US, 57 CA, 64 UK; "block corn" 1; "adult content blocker" 1; "temptation blocker" 2. The release-impact view compares against these two weeks after the version is live. What to expect week by week.
The goals
Three, set on November 3: "site blocker" from 17 into the top 10, "quit corn" from 11 into the top 10, and "web filter", which the field covers and the app does not yet rank for, into the top 50. How goals are chosen.
The screenshots
Two sets, built to the same seven rules, one feature-led and one outcome-led, for a Product Page Optimization test once the listing is stable. The rules and the test.
What comes next
The two-week review of version 5.12, with the three removed words' terms and the three added words' terms side by side. The result of the goals. A Canadian field tuned for the storefront that already ranks best. Each lands here when it is measured, with the date, and nowhere before.
Download on theApp StoreFree to start. No account. Data Not Collected.
Questions
Is this case study real?
Yes. Escape is our own app, the ranks are RankASO's daily readings for the dates shown, and the keyword fields are the ones entered in App Store Connect. The page is updated after each version is measured.
Why publish your own keyword field?
Because the method is the point, and a method without the real numbers is a claim. The field earns what it earns whether or not it is public.
How can I follow the same method for my app?
Track thirty terms in two storefronts, read the field report after a week, change one thing per version with a baseline, and review at two weeks. The ten-minute setup.